----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  C:\Users\eddie\OneDrive\CURRENT\Research\BuyingGlobalization\P&P\log.log
  log type:  text
 opened on:   2 Sep 2019, 20:40:38

. 
. *     ***************************************************************** *
. *     ***************************************************************** *
. *     File-Name:  do.do                                                 *
. *     Date:       September 2019                                        *
. *     Author:     Eddie Hearn                                           *
. *     Purpose:    Buying Globalization                                  *
. *     Input File: DATA.dta                                              *
. *     Output File: log.log                                              *
. *     Data Output: None                                                 *
. *     Program:    Stata 14
. *     Machine:    Home                                                  *
. *     ****************************************************************  *
. *     ****************************************************************  *
. 
. 
. use C:\Users\eddie\OneDrive\CURRENT\Research\BuyingGlobalization\P&P\DATA.dta 

. 
. 
. 
. *     ****************************************************************  *
. *          Generate Variables                                           *
. *     ****************************************************************  *
. 
. gen TRADE = 1 if Q13B ==1
(677 missing values generated)

. replace TRADE = 2 if Q13B ==3
(212 real changes made)

. replace TRADE = 3 if Q13B ==2
(382 real changes made)

. gen AGE = QF1 if QF1<9
(7 missing values generated)

. gen GRAD = 0 if QF2 <7
(3 missing values generated)

. replace GRAD = 1 if QF2 ==5|QF2 ==6
(424 real changes made)

. gen income = QF8 if QF8 < 8
(85 missing values generated)

. sum income, detail

                           income
-------------------------------------------------------------
      Percentiles      Smallest
 1%            1              1
 5%            1              1
10%            1              1       Obs                 915
25%            2              1       Sum of Wgt.         915

50%            4                      Mean           3.798907
                        Largest       Std. Dev.       1.79136
75%            5              7
90%            6              7       Variance        3.20897
95%            7              7       Skewness       .0759555
99%            7              7       Kurtosis       2.024087

. gen LOW = 1 if income<4
(603 missing values generated)

. replace LOW = 0 if income>3
(603 real changes made)

. replace LOW = . if income ==.
(85 real changes made, 85 to missing)

. drop income

. gen UNEMP = 0 if Q8 <7
(8 missing values generated)

. replace UNEMP =1 if Q8 ==3
(48 real changes made)

. gen POLIT = 1 if QF7 ==1
(848 missing values generated)

. replace POLIT = 2 if QF7 ==3
(538 real changes made)

. replace POLIT = 3 if QF7 ==2
(293 real changes made)

. gen FEMALE  = qa -1

. gen SOCIO = 1 if Q4A1==1| Q4A1==3
(608 missing values generated)

. replace SOCIO = 1 if Q4A2==1| Q4A2==3
(248 real changes made)

. recode SOCIO (.=0)
(SOCIO: 360 changes made)

. gen CHILD = Q16C if Q16C<5
(23 missing values generated)

. gen ENVIRO = Q16D if Q16D<5
(37 missing values generated)

. gen UNION = Q16F if Q16F<5
(49 missing values generated)

. gen POST = ((CHILD + ENVIRO + UNION)*-1) +13
(85 missing values generated)

. drop CHILD ENVIRO UNION

. gen JPN = 1 if Q21A==2
(734 missing values generated)

. replace JPN = 2 if Q21A==3
(462 real changes made)

. replace JPN = 3 if Q21A==1
(261 real changes made)

. 
. gen ROK = 1 if Q21B==2
(601 missing values generated)

. replace ROK = 2 if Q21B==3
(442 real changes made)

. replace ROK = 3 if Q21B==1
(143 real changes made)

. 
. 
. gen WE = 1 if Q21C==2
(819 missing values generated)

. replace WE = 2 if Q21C==3
(531 real changes made)

. replace WE = 3 if Q21C==1
(262 real changes made)

. 
. gen EE = 1 if Q21D==2
(747 missing values generated)

. replace EE = 2 if Q21D==3
(547 real changes made)

. replace EE = 3 if Q21D==1
(173 real changes made)

. 
. gen AF = 1 if Q21E==2
(694 missing values generated)

. replace AF= 2 if Q21E==3
(496 real changes made)

. replace AF = 3 if Q21E==1
(155 real changes made)

. 
. gen IN = 1 if Q21F==2
(620 missing values generated)

. replace IN= 2 if Q21F==3
(495 real changes made)

. replace IN = 3 if Q21F==1
(105 real changes made)

. 
. gen CHU = 1 if Q21G==2
(387 missing values generated)

. replace CHU= 2 if Q21G==3
(273 real changes made)

. replace CHU = 3 if Q21G==1
(99 real changes made)

. 
. gen MEX = 1 if Q21H==2
(612 missing values generated)

. replace MEX= 2 if Q21H==3
(459 real changes made)

. replace MEX = 3 if Q21H==1
(138 real changes made)

. 
. gen CAN = 1 if Q21I==2
(911 missing values generated)

. replace CAN= 2 if Q21I==3
(514 real changes made)

. replace CAN = 3 if Q21I==1
(382 real changes made)

. 
. gen CATEGORY = (JPN + ROK + WE + EE + AF + IN + CHU + MEX + CAN) - 8 
(90 missing values generated)

. gen CON_OR = (JPN + ROK + WE + EE + AF + IN + CHU + MEX + CAN) - 18 
(90 missing values generated)

. gen REDUCED = (JPN + ROK + WE + EE + AF + IN  + MEX + CAN)- 16 
(87 missing values generated)

. 
. drop JPN ROK  WE  EE  AF  IN CHU  MEX  CAN

. 
. 
. *     ****************************************************************  *
. *          Models 1-3  MNM LOGIT                                        *
. *     ****************************************************************  *
. 
. mlogit TRADE CON_OR, robust base(2)

Iteration 0:   log pseudolikelihood = -904.03878  
Iteration 1:   log pseudolikelihood = -876.46468  
Iteration 2:   log pseudolikelihood =  -876.2482  
Iteration 3:   log pseudolikelihood = -876.24816  

Multinomial logistic regression                 Number of obs     =        845
                                                Wald chi2(2)      =      45.77
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -876.24816               Pseudo R2         =     0.0307

------------------------------------------------------------------------------
             |               Robust
       TRADE |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
1            |
      CON_OR |    .093674     .02826     3.31   0.001     .0382853    .1490626
       _cons |   .5274527   .0962719     5.48   0.000     .3387633    .7161421
-------------+----------------------------------------------------------------
2            |  (base outcome)
-------------+----------------------------------------------------------------
3            |
      CON_OR |  -.0912874    .030257    -3.02   0.003      -.15059   -.0319848
       _cons |   .4443839   .1020505     4.35   0.000     .2443685    .6443993
------------------------------------------------------------------------------

. mlogit TRADE CON_OR GRAD  LOW UNEMP FEMALE  AGE POLIT, robust base(2)

Iteration 0:   log pseudolikelihood = -823.54142  
Iteration 1:   log pseudolikelihood = -772.38225  
Iteration 2:   log pseudolikelihood = -772.09613  
Iteration 3:   log pseudolikelihood = -772.09606  

Multinomial logistic regression                 Number of obs     =        773
                                                Wald chi2(14)     =      83.18
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -772.09606               Pseudo R2         =     0.0625

------------------------------------------------------------------------------
             |               Robust
       TRADE |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
1            |
      CON_OR |   .0576916   .0316969     1.82   0.069    -.0044332    .1198164
        GRAD |    .471691     .20814     2.27   0.023     .0637442    .8796379
         LOW |   .1338446   .2169661     0.62   0.537    -.2914011    .5590904
       UNEMP |   .1885439     .51092     0.37   0.712    -.8128408    1.189929
      FEMALE |  -.6512876   .2047382    -3.18   0.001    -1.052567   -.2500082
         AGE |  -.2081089   .0569438    -3.65   0.000    -.3197167   -.0965012
       POLIT |   .1477386   .1561684     0.95   0.344    -.1583458     .453823
       _cons |   1.477369   .5170087     2.86   0.004     .4640507    2.490688
-------------+----------------------------------------------------------------
2            |  (base outcome)
-------------+----------------------------------------------------------------
3            |
      CON_OR |  -.0989041   .0335001    -2.95   0.003    -.1645631   -.0332451
        GRAD |  -.2032326   .2045385    -0.99   0.320    -.6041206    .1976555
         LOW |   .4366021   .2069466     2.11   0.035     .0309941    .8422101
       UNEMP |    .145071   .5153689     0.28   0.778    -.8650335    1.155175
      FEMALE |  -.2805861   .2013844    -1.39   0.164    -.6752922    .1141201
         AGE |  -.1175367   .0568937    -2.07   0.039    -.2290463   -.0060271
       POLIT |  -.0569856   .1498944    -0.38   0.704    -.3507732    .2368021
       _cons |      1.351   .5047409     2.68   0.007     .3617262    2.340274
------------------------------------------------------------------------------

. mlogit TRADE CON_OR GRAD  LOW UNEMP FEMALE  AGE POLIT SOCIO POST, robust base(2)

Iteration 0:   log pseudolikelihood = -778.32172  
Iteration 1:   log pseudolikelihood = -712.67969  
Iteration 2:   log pseudolikelihood = -712.34696  
Iteration 3:   log pseudolikelihood = -712.34669  
Iteration 4:   log pseudolikelihood = -712.34669  

Multinomial logistic regression                 Number of obs     =        737
                                                Wald chi2(18)     =     107.57
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -712.34669               Pseudo R2         =     0.0848

------------------------------------------------------------------------------
             |               Robust
       TRADE |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
1            |
      CON_OR |   .0551864   .0328849     1.68   0.093    -.0092669    .1196396
        GRAD |   .3804678   .2161435     1.76   0.078    -.0431656    .8041011
         LOW |   .1569242   .2260829     0.69   0.488    -.2861901    .6000386
       UNEMP |   .0932488   .5101129     0.18   0.855     -.906554    1.093052
      FEMALE |  -.6015083   .2154992    -2.79   0.005    -1.023879   -.1791376
         AGE |  -.1935934   .0594834    -3.25   0.001    -.3101786   -.0770081
       POLIT |   .2609039   .1649926     1.58   0.114    -.0624756    .5842835
       SOCIO |  -.3579737   .2146193    -1.67   0.095    -.7786198    .0626724
        POST |   .0038368    .049696     0.08   0.938    -.0935655    .1012392
       _cons |    1.43474   .6966199     2.06   0.039     .0693898    2.800089
-------------+----------------------------------------------------------------
2            |  (base outcome)
-------------+----------------------------------------------------------------
3            |
      CON_OR |  -.1019899   .0353976    -2.88   0.004     -.171368   -.0326118
        GRAD |  -.1815847   .2139452    -0.85   0.396    -.6009095    .2377402
         LOW |   .4228816   .2178255     1.94   0.052    -.0040485    .8498117
       UNEMP |  -.0177077   .5298234    -0.03   0.973    -1.056143    1.020727
      FEMALE |  -.3594406   .2133952    -1.68   0.092    -.7776875    .0588063
         AGE |  -.0990289   .0599753    -1.65   0.099    -.2165784    .0185206
       POLIT |   .1576548   .1634864     0.96   0.335    -.1627727    .4780823
       SOCIO |   .4251983   .2197034     1.94   0.053    -.0054125    .8558091
        POST |    .146854   .0538818     2.73   0.006     .0412477    .2524603
       _cons |  -.4204834   .7236422    -0.58   0.561    -1.838796    .9978291
------------------------------------------------------------------------------

. estimates store IV_ORDINAL

. 
. *     ****************************************************************  *
. *          Models 1-3  ORDINAL LOGIT                                    *
. *     ****************************************************************  *
. 
. ologit TRADE CON_OR, robust

Iteration 0:   log pseudolikelihood = -904.03878  
Iteration 1:   log pseudolikelihood = -876.57486  
Iteration 2:   log pseudolikelihood = -876.51551  
Iteration 3:   log pseudolikelihood = -876.51551  

Ordered logistic regression                     Number of obs     =        845
                                                Wald chi2(1)      =      47.53
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -876.51551               Pseudo R2         =     0.0304

------------------------------------------------------------------------------
             |               Robust
       TRADE |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      CON_OR |  -.1467997   .0212933    -6.89   0.000    -.1885338   -.1050656
-------------+----------------------------------------------------------------
       /cut1 |  -.4212736   .0770578                     -.5723041   -.2702432
       /cut2 |   .5584243   .0789218                      .4037404    .7131082
------------------------------------------------------------------------------

. ologit TRADE CON_OR GRAD  LOW UNEMP FEMALE  AGE POLIT, robust 

Iteration 0:   log pseudolikelihood = -823.54142  
Iteration 1:   log pseudolikelihood = -783.18115  
Iteration 2:   log pseudolikelihood = -783.04659  
Iteration 3:   log pseudolikelihood = -783.04655  

Ordered logistic regression                     Number of obs     =        773
                                                Wald chi2(7)      =      72.13
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -783.04655               Pseudo R2         =     0.0492

------------------------------------------------------------------------------
             |               Robust
       TRADE |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      CON_OR |  -.1242119    .023097    -5.38   0.000    -.1694813   -.0789426
        GRAD |   -.537888   .1498223    -3.59   0.000    -.8315343   -.2442418
         LOW |   .2353674   .1516979     1.55   0.121    -.0619551    .5326898
       UNEMP |   -.018659   .3409693    -0.05   0.956    -.6869464    .6496285
      FEMALE |   .2669061   .1397532     1.91   0.056    -.0070051    .5408173
         AGE |   .0722701   .0345317     2.09   0.036     .0045892    .1399509
       POLIT |  -.1714621   .1047575    -1.64   0.102    -.3767831    .0338588
-------------+----------------------------------------------------------------
       /cut1 |  -.3917015   .3394496                      -1.05701    .2736075
       /cut2 |   .5818487   .3396169                     -.0837882    1.247486
------------------------------------------------------------------------------

. ologit TRADE CON_OR GRAD  LOW UNEMP FEMALE  AGE POLIT SOCIO POST, robust

Iteration 0:   log pseudolikelihood = -778.32172  
Iteration 1:   log pseudolikelihood = -723.30646  
Iteration 2:   log pseudolikelihood = -723.08286  
Iteration 3:   log pseudolikelihood = -723.08281  

Ordered logistic regression                     Number of obs     =        737
                                                Wald chi2(9)      =      96.54
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -723.08281               Pseudo R2         =     0.0710

------------------------------------------------------------------------------
             |               Robust
       TRADE |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      CON_OR |   -.128544   .0240266    -5.35   0.000    -.1756354   -.0814526
        GRAD |  -.4456842   .1529115    -2.91   0.004    -.7453852   -.1459831
         LOW |   .2199132   .1560891     1.41   0.159    -.0860158    .5258423
       UNEMP |  -.0993653   .3601353    -0.28   0.783    -.8052176     .606487
      FEMALE |    .165536   .1459764     1.13   0.257    -.1205725    .4516445
         AGE |   .0742788   .0373198     1.99   0.047     .0011334    .1474242
       POLIT |  -.0738672   .1137153    -0.65   0.516    -.2967451    .1490106
       SOCIO |   .6255282   .1545646     4.05   0.000     .3225871    .9284693
        POST |   .1195724   .0373225     3.20   0.001     .0464217    .1927231
-------------+----------------------------------------------------------------
       /cut1 |    1.06187   .4909813                      .0995645    2.024176
       /cut2 |   2.002341   .4957208                      1.030746    2.973936
------------------------------------------------------------------------------

. 
. 
. *     ****************************************************************  *
. *          Models 1-3  Reduced Measure - Excluding CHINA                *
. *     ****************************************************************  *
. 
. mlogit TRADE REDUCED, robust base(2)

Iteration 0:   log pseudolikelihood = -907.42643  
Iteration 1:   log pseudolikelihood = -884.48253  
Iteration 2:   log pseudolikelihood =  -884.3107  
Iteration 3:   log pseudolikelihood = -884.31067  

Multinomial logistic regression                 Number of obs     =        848
                                                Wald chi2(2)      =      39.02
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -884.31067               Pseudo R2         =     0.0255

------------------------------------------------------------------------------
             |               Robust
       TRADE |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
1            |
     REDUCED |   .0959599   .0299578     3.20   0.001     .0372437    .1546761
       _cons |   .4848636   .0935777     5.18   0.000     .3014547    .6682725
-------------+----------------------------------------------------------------
2            |  (base outcome)
-------------+----------------------------------------------------------------
3            |
     REDUCED |  -.0836523   .0319798    -2.62   0.009    -.1463315   -.0209731
       _cons |   .5034213   .0958136     5.25   0.000     .3156301    .6912126
------------------------------------------------------------------------------

. mlogit TRADE REDUCED GRAD  LOW UNEMP FEMALE  AGE POLIT, robust base(2)

Iteration 0:   log pseudolikelihood = -825.42395  
Iteration 1:   log pseudolikelihood = -779.01742  
Iteration 2:   log pseudolikelihood =  -778.7565  
Iteration 3:   log pseudolikelihood = -778.75646  

Multinomial logistic regression                 Number of obs     =        775
                                                Wald chi2(14)     =      78.20
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -778.75646               Pseudo R2         =     0.0565

------------------------------------------------------------------------------
             |               Robust
       TRADE |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
1            |
     REDUCED |   .0585598   .0336465     1.74   0.082     -.007386    .1245057
        GRAD |   .4545337   .2085464     2.18   0.029     .0457904     .863277
         LOW |   .1455127   .2162224     0.67   0.501    -.2782754    .5693009
       UNEMP |   .1602384   .5086901     0.32   0.753    -.8367759    1.157253
      FEMALE |  -.6424476   .2042865    -3.14   0.002    -1.042842   -.2420534
         AGE |  -.2093129   .0568075    -3.68   0.000    -.3206536   -.0979722
       POLIT |   .1474225   .1560207     0.94   0.345    -.1583725    .4532175
       _cons |    1.46638   .5163032     2.84   0.005     .4544441    2.478316
-------------+----------------------------------------------------------------
2            |  (base outcome)
-------------+----------------------------------------------------------------
3            |
     REDUCED |  -.0874456   .0353777    -2.47   0.013    -.1567845   -.0181066
        GRAD |  -.1966748   .2049611    -0.96   0.337    -.5983911    .2050416
         LOW |   .4440601   .2061855     2.15   0.031      .039944    .8481762
       UNEMP |   .2296498   .5121206     0.45   0.654    -.7740882    1.233388
      FEMALE |  -.2776468   .2007855    -1.38   0.167    -.6711792    .1158857
         AGE |  -.1104832   .0565857    -1.95   0.051    -.2213892    .0004228
       POLIT |  -.0472094   .1496401    -0.32   0.752    -.3404986    .2460799
       _cons |   1.348103   .5051165     2.67   0.008     .3580929    2.338113
------------------------------------------------------------------------------

. mlogit TRADE REDUCED GRAD  LOW UNEMP FEMALE  AGE POLIT SOCIO POST, robust base(2)

Iteration 0:   log pseudolikelihood =  -780.1656  
Iteration 1:   log pseudolikelihood = -717.58502  
Iteration 2:   log pseudolikelihood = -717.28446  
Iteration 3:   log pseudolikelihood = -717.28425  
Iteration 4:   log pseudolikelihood = -717.28425  

Multinomial logistic regression                 Number of obs     =        739
                                                Wald chi2(18)     =     105.18
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -717.28425               Pseudo R2         =     0.0806

------------------------------------------------------------------------------
             |               Robust
       TRADE |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
1            |
     REDUCED |   .0603278   .0355939     1.69   0.090    -.0094349    .1300905
        GRAD |   .3585459   .2170625     1.65   0.099    -.0668888    .7839806
         LOW |   .1713326   .2253942     0.76   0.447    -.2704319    .6130972
       UNEMP |   .0723591    .507651     0.14   0.887    -.9226186    1.067337
      FEMALE |  -.5907088     .21495    -2.75   0.006    -1.012003   -.1694146
         AGE |  -.1938693   .0592889    -3.27   0.001    -.3100735   -.0776651
       POLIT |   .2614404   .1648977     1.59   0.113    -.0617532    .5846341
       SOCIO |  -.3602095   .2147562    -1.68   0.093    -.7811238    .0607049
        POST |   .0031324   .0494764     0.06   0.950    -.0938395    .1001044
       _cons |   1.425296   .6916363     2.06   0.039     .0697138    2.780878
-------------+----------------------------------------------------------------
2            |  (base outcome)
-------------+----------------------------------------------------------------
3            |
     REDUCED |  -.0959759   .0377074    -2.55   0.011     -.169881   -.0220707
        GRAD |  -.1685079    .214687    -0.78   0.433    -.5892867     .252271
         LOW |   .4276286   .2173684     1.97   0.049     .0015944    .8536628
       UNEMP |   .0618366   .5240738     0.12   0.906    -.9653291    1.089002
      FEMALE |  -.3641621   .2128986    -1.71   0.087    -.7814356    .0531114
         AGE |  -.0931147    .059678    -1.56   0.119    -.2100814    .0238521
       POLIT |   .1706182   .1633516     1.04   0.296     -.149545    .4907814
       SOCIO |    .441133   .2192296     2.01   0.044     .0114509    .8708152
        POST |   .1521264   .0535842     2.84   0.005     .0471032    .2571496
       _cons |  -.4741727   .7209232    -0.66   0.511    -1.887156    .9388108
------------------------------------------------------------------------------

. 
. 
. *     ****************************************************************  *
. *           Consumer Orientation (Ordinal vs Categorical)               *
. *     ****************************************************************  *
. 
. mlogit TRADE i.CATEGORY GRAD  LOW UNEMP FEMALE  AGE POLIT SOCIO POST, robust base(2)

Iteration 0:   log pseudolikelihood = -778.32172  
Iteration 1:   log pseudolikelihood = -687.53732  
Iteration 2:   log pseudolikelihood =  -685.4325  
Iteration 3:   log pseudolikelihood = -685.06225  
Iteration 4:   log pseudolikelihood = -684.99564  
Iteration 5:   log pseudolikelihood = -684.98031  
Iteration 6:   log pseudolikelihood = -684.97686  
Iteration 7:   log pseudolikelihood = -684.97603  
Iteration 8:   log pseudolikelihood = -684.97586  
Iteration 9:   log pseudolikelihood = -684.97583  

Multinomial logistic regression                 Number of obs     =        737
                                                Wald chi2(51)     =          .
                                                Prob > chi2       =          .
Log pseudolikelihood = -684.97583               Pseudo R2         =     0.1199

------------------------------------------------------------------------------
             |               Robust
       TRADE |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
1            |
    CATEGORY |
          2  |  -12.39616   1.194165   -10.38   0.000    -14.73669   -10.05564
          3  |   .6838496   1.012203     0.68   0.499    -1.300032    2.667731
          4  |   1.186551   .9914409     1.20   0.231    -.7566374     3.12974
          5  |   .8777532   .9323263     0.94   0.346    -.9495728    2.705079
          6  |   1.252466   .7743122     1.62   0.106    -.2651578     2.77009
          7  |   .7129685   .7713212     0.92   0.355    -.7987933     2.22473
          8  |   1.866552   .7677564     2.43   0.015     .3617771    3.371327
          9  |   1.295023   .6788132     1.91   0.056    -.0354263    2.625473
         10  |   1.038487   .6527684     1.59   0.112    -.2409154     2.31789
         11  |   1.099536   .7035416     1.56   0.118    -.2793797    2.478453
         12  |   1.268276   .7728402     1.64   0.101    -.2464632    2.783015
         13  |   1.394309    .804083     1.73   0.083    -.1816644    2.970283
         14  |   .3756512   1.023064     0.37   0.713    -1.629517     2.38082
         15  |   1.492971   .8604586     1.74   0.083    -.1934965    3.179439
         16  |   15.61757   .8996465    17.36   0.000     13.85429    17.38084
         17  |   15.24842   .8248699    18.49   0.000     13.63171    16.86514
         18  |   16.51812   1.232492    13.40   0.000     14.10248    18.93376
         19  |   .5062277   1.376242     0.37   0.713    -2.191156    3.203612
             |
        GRAD |   .3460557   .2183332     1.58   0.113    -.0818694    .7739808
         LOW |   .1584755   .2299917     0.69   0.491    -.2922999     .609251
       UNEMP |   .1020956    .506677     0.20   0.840     -.890973    1.095164
      FEMALE |  -.5957083   .2178801    -2.73   0.006    -1.022746   -.1686711
         AGE |  -.1902478   .0591514    -3.22   0.001    -.3061825   -.0743131
       POLIT |    .214498   .1674942     1.28   0.200    -.1137846    .5427807
       SOCIO |  -.3632066   .2186662    -1.66   0.097    -.7917845    .0653713
        POST |  -.0214795   .0508205    -0.42   0.673    -.1210859    .0781269
       _cons |   .5212992   .9311493     0.56   0.576     -1.30372    2.346318
-------------+----------------------------------------------------------------
2            |  (base outcome)
-------------+----------------------------------------------------------------
3            |
    CATEGORY |
          2  |   1.254777   1.221023     1.03   0.304    -1.138385    3.647939
          3  |   .2375384   .7628867     0.31   0.756    -1.257692    1.732769
          4  |   .0363459   .8362963     0.04   0.965    -1.602765    1.675457
          5  |   1.326218   .7009926     1.89   0.059    -.0477023    2.700138
          6  |   .4310179   .6112914     0.71   0.481    -.7670912    1.629127
          7  |   .6114812   .5862742     1.04   0.297    -.5375951    1.760557
          8  |   .3355808    .623425     0.54   0.590    -.8863099    1.557471
          9  |  -.3170169   .5430832    -0.58   0.559     -1.38144    .7474066
         10  |  -.6436633   .5135845    -1.25   0.210    -1.650271    .3629439
         11  |  -.4255283   .5720562    -0.74   0.457    -1.546738    .6956813
         12  |  -1.035494   .7467535    -1.39   0.166    -2.499104    .4281157
         13  |   .1415216   .6851398     0.21   0.836    -1.201328    1.484371
         14  |  -1.075706   .9045956    -1.19   0.234    -2.848681    .6972686
         15  |  -1.475742   .9944843    -1.48   0.138    -3.424895    .4734115
         16  |   13.59343   1.356572    10.02   0.000     10.93459    16.25226
         17  |   14.10392   .6765854    20.85   0.000     12.77784       15.43
         18  |  -.4651205   .5914311    -0.79   0.432    -1.624304    .6940633
         19  |  -.0741656   1.360212    -0.05   0.957    -2.740132    2.591801
             |
        GRAD |   -.191768   .2213251    -0.87   0.386    -.6255572    .2420213
         LOW |   .4167544   .2278221     1.83   0.067    -.0297687    .8632775
       UNEMP |   .0263391   .5221353     0.05   0.960    -.9970273    1.049706
      FEMALE |  -.3658129   .2192513    -1.67   0.095    -.7955375    .0639117
         AGE |  -.1044549   .0601575    -1.74   0.083    -.2223614    .0134516
       POLIT |   .0815225    .171617     0.48   0.635    -.2548408    .4178857
       SOCIO |   .3293856   .2282583     1.44   0.149    -.1179925    .7767637
        POST |   .1173012   .0545186     2.15   0.031     .0104467    .2241556
       _cons |   .2705041   .9000189     0.30   0.764    -1.493501    2.034509
------------------------------------------------------------------------------
Note: 1 observation completely determined.  Standard errors questionable.

. estimates store IV_CATEGORICAL

. estimates stats IV_ORDINAL IV_CATEGORICAL

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
  IV_ORDINAL |        737 -778.3217  -712.3467      20    1464.693   1556.745
IV_CATEGOR~L |        737 -778.3217  -684.9758      53    1475.952   1719.889
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. 
. *     ****************************************************************  *
. *            Hausman-McFadden Test                                      *
. *     ****************************************************************  *
. 
. mlogit TRADE CON_OR GRAD  LOW UNEMP FEMALE  AGE POLIT SOCIO POST

Iteration 0:   log likelihood = -778.32172  
Iteration 1:   log likelihood = -712.67969  
Iteration 2:   log likelihood = -712.34696  
Iteration 3:   log likelihood = -712.34669  
Iteration 4:   log likelihood = -712.34669  

Multinomial logistic regression                 Number of obs     =        737
                                                LR chi2(18)       =     131.95
                                                Prob > chi2       =     0.0000
Log likelihood = -712.34669                     Pseudo R2         =     0.0848

------------------------------------------------------------------------------
       TRADE |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
1            |
      CON_OR |   .1571762   .0297681     5.28   0.000     .0988319    .2155206
        GRAD |   .5620524   .1880477     2.99   0.003     .1934858    .9306191
         LOW |  -.2659574   .1913157    -1.39   0.164    -.6409292    .1090145
       UNEMP |   .1109565   .4170988     0.27   0.790    -.7065421    .9284552
      FEMALE |  -.2420677   .1828341    -1.32   0.186    -.6004159    .1162806
         AGE |  -.0945645   .0449541    -2.10   0.035    -.1826729   -.0064561
       POLIT |   .1032491   .1442376     0.72   0.474    -.1794513    .3859496
       SOCIO |   -.783172   .1886959    -4.15   0.000    -1.153009   -.4133348
        POST |  -.1430171   .0452015    -3.16   0.002    -.2316105   -.0544238
       _cons |   1.855223    .593804     3.12   0.002     .6913887    3.019057
-------------+----------------------------------------------------------------
2            |
      CON_OR |   .1019899   .0325693     3.13   0.002     .0381552    .1658246
        GRAD |   .1815847   .2181465     0.83   0.405    -.2459745    .6091439
         LOW |  -.4228816   .2194169    -1.93   0.054    -.8529309    .0071677
       UNEMP |   .0177077   .5095682     0.03   0.972    -.9810276    1.016443
      FEMALE |   .3594406    .211944     1.70   0.090     -.055962    .7748432
         AGE |   .0990289   .0567683     1.74   0.081    -.0122349    .2102927
       POLIT |  -.1576548   .1625081    -0.97   0.332    -.4761649    .1608553
       SOCIO |  -.4251983   .2227917    -1.91   0.056    -.8618619    .0114654
        POST |   -.146854   .0509484    -2.88   0.004     -.246711   -.0469969
       _cons |   .4204834   .6894025     0.61   0.542    -.9307205    1.771687
-------------+----------------------------------------------------------------
3            |  (base outcome)
------------------------------------------------------------------------------

. estimates store FULL

. 
. mlogit TRADE CON_OR GRAD  LOW UNEMP FEMALE  AGE POLIT SOCIO POST if TRADE!=2

Iteration 0:   log likelihood = -405.94931  
Iteration 1:   log likelihood = -351.24423  
Iteration 2:   log likelihood = -351.07929  
Iteration 3:   log likelihood = -351.07919  
Iteration 4:   log likelihood = -351.07919  

Multinomial logistic regression                 Number of obs     =        587
                                                LR chi2(9)        =     109.74
                                                Prob > chi2       =     0.0000
Log likelihood = -351.07919                     Pseudo R2         =     0.1352

------------------------------------------------------------------------------
       TRADE |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
1            |
      CON_OR |   .1543422   .0304808     5.06   0.000     .0946008    .2140835
        GRAD |   .5787767   .1895959     3.05   0.002     .2071755    .9503778
         LOW |  -.2755722    .194168    -1.42   0.156    -.6561345    .1049901
       UNEMP |   .1059514   .4216202     0.25   0.802    -.7204089    .9323118
      FEMALE |  -.2527801   .1865299    -1.36   0.175    -.6183719    .1128117
         AGE |  -.0977199   .0462442    -2.11   0.035    -.1883569   -.0070828
       POLIT |   .0960809   .1465099     0.66   0.512    -.1910733     .383235
       SOCIO |  -.8083076   .1901375    -4.25   0.000     -1.18097   -.4356448
        POST |   -.141399   .0457104    -3.09   0.002    -.2309897   -.0518083
       _cons |    1.89118   .6025503     3.14   0.002      .710203    3.072157
-------------+----------------------------------------------------------------
3            |  (base outcome)
------------------------------------------------------------------------------

. estimates store NUETRAL_ELIMINATED

. 
. mlogit TRADE CON_OR GRAD  LOW UNEMP FEMALE  AGE POLIT SOCIO POST if TRADE!=1

Iteration 0:   log likelihood = -290.43148  
Iteration 1:   log likelihood = -273.35944  
Iteration 2:   log likelihood = -273.12689  
Iteration 3:   log likelihood = -273.12668  
Iteration 4:   log likelihood = -273.12668  

Multinomial logistic regression                 Number of obs     =        460
                                                LR chi2(9)        =      34.61
                                                Prob > chi2       =     0.0001
Log likelihood = -273.12668                     Pseudo R2         =     0.0596

------------------------------------------------------------------------------
       TRADE |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
2            |
      CON_OR |   .0952529   .0314837     3.03   0.002     .0335461    .1569598
        GRAD |   .1773236   .2217895     0.80   0.424    -.2573758     .612023
         LOW |  -.4595567   .2240166    -2.05   0.040    -.8986211   -.0204923
       UNEMP |   -.074203   .5143974    -0.14   0.885    -1.082404    .9339974
      FEMALE |   .3295581   .2149192     1.53   0.125    -.0916759     .750792
         AGE |   .1126398   .0590144     1.91   0.056    -.0030263     .228306
       POLIT |  -.1668058   .1670673    -1.00   0.318    -.4942516    .1606401
       SOCIO |  -.4557921   .2284875    -1.99   0.046    -.9036193   -.0079649
        POST |  -.1374889   .0486249    -2.83   0.005    -.2327919   -.0421859
       _cons |   .3392888   .6794667     0.50   0.618    -.9924415    1.671019
-------------+----------------------------------------------------------------
3            |  (base outcome)
------------------------------------------------------------------------------

. estimates store BEN_ELIMINATED

. 
. hausman FULL  NUETRAL_ELIMINATED, alleqs constant

                 ---- Coefficients ----
             |      (b)          (B)            (b-B)     sqrt(diag(V_b-V_B))
             |      FULL     NUETRAL_EL~D    Difference          S.E.
-------------+----------------------------------------------------------------
      CON_OR |    .1571762     .1543422        .0028341               .
        GRAD |    .5620524     .5787767       -.0167242               .
         LOW |   -.2659574    -.2755722        .0096149               .
       UNEMP |    .1109565     .1059514        .0050051               .
      FEMALE |   -.2420677    -.2527801        .0107125               .
         AGE |   -.0945645    -.0977199        .0031554               .
       POLIT |    .1032491     .0960809        .0071683               .
       SOCIO |    -.783172    -.8083076        .0251355               .
        POST |   -.1430171     -.141399       -.0016181               .
       _cons |    1.855223      1.89118       -.0359568               .
------------------------------------------------------------------------------
                          b = consistent under Ho and Ha; obtained from mlogit
           B = inconsistent under Ha, efficient under Ho; obtained from mlogit

    Test:  Ho:  difference in coefficients not systematic

                 chi2(10) = (b-B)'[(V_b-V_B)^(-1)](b-B)
                          =        2.03
                Prob>chi2 =      0.9961
                (V_b-V_B is not positive definite)

. hausman FULL  BEN_ELIMINATED, alleqs constant

                 ---- Coefficients ----
             |      (b)          (B)            (b-B)     sqrt(diag(V_b-V_B))
             |      FULL     BEN_ELIMIN~D    Difference          S.E.
-------------+----------------------------------------------------------------
      CON_OR |    .1019899     .0952529         .006737        .0083391
        GRAD |    .1815847     .1773236        .0042611               .
         LOW |   -.4228816    -.4595567        .0366751               .
       UNEMP |    .0177077     -.074203        .0919108               .
      FEMALE |    .3594406     .3295581        .0298825               .
         AGE |    .0990289     .1126398        -.013611               .
       POLIT |   -.1576548    -.1668058         .009151               .
       SOCIO |   -.4251983    -.4557921        .0305938               .
        POST |    -.146854    -.1374889       -.0093651        .0152106
       _cons |    .4204834     .3392888        .0811947        .1166222
------------------------------------------------------------------------------
                          b = consistent under Ho and Ha; obtained from mlogit
           B = inconsistent under Ha, efficient under Ho; obtained from mlogit

    Test:  Ho:  difference in coefficients not systematic

                 chi2(10) = (b-B)'[(V_b-V_B)^(-1)](b-B)
                          =        0.61
                Prob>chi2 =      1.0000
                (V_b-V_B is not positive definite)

. 
. 
. *     ****************************************************************  *
. *       Install  Clarify                                                *
. *     ****************************************************************  *
. 
. 
. **** net from https://gking.harvard.edu/clarify/  ****
. **** net get clarify                              ****                          
. 
. *     ****************************************************************  *
. *       Predicted Prob w/ Clarify                                       *
. *     ****************************************************************  *
.         
. estsimp mlogit TRADE CON_OR GRAD  LOW UNEMP FEMALE  AGE POLIT SOCIO POST, robust base(2)

Iteration 0:   log pseudolikelihood = -778.32172
Iteration 1:   log pseudolikelihood = -713.24032
Iteration 2:   log pseudolikelihood = -712.34848
Iteration 3:   log pseudolikelihood = -712.34669
Iteration 4:   log pseudolikelihood = -712.34669

Multinomial logistic regression                   Number of obs   =        737
                                                  Wald chi2(18)   =     107.57
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -712.34669                 Pseudo R2       =     0.0848

------------------------------------------------------------------------------
             |               Robust
       TRADE |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
1            |
      CON_OR |   .0551864   .0328849     1.68   0.093    -.0092669    .1196396
        GRAD |   .3804678   .2161435     1.76   0.078    -.0431656    .8041011
         LOW |   .1569242   .2260829     0.69   0.488    -.2861901    .6000386
       UNEMP |   .0932488   .5101129     0.18   0.855     -.906554    1.093052
      FEMALE |  -.6015083   .2154992    -2.79   0.005    -1.023879   -.1791376
         AGE |  -.1935934   .0594834    -3.25   0.001    -.3101786   -.0770081
       POLIT |   .2609039   .1649926     1.58   0.114    -.0624756    .5842835
       SOCIO |  -.3579737   .2146193    -1.67   0.095    -.7786198    .0626724
        POST |   .0038368    .049696     0.08   0.938    -.0935655    .1012392
       _cons |    1.43474   .6966199     2.06   0.039     .0693898    2.800089
-------------+----------------------------------------------------------------
3            |
      CON_OR |  -.1019899   .0353976    -2.88   0.004     -.171368   -.0326118
        GRAD |  -.1815847   .2139452    -0.85   0.396    -.6009095    .2377402
         LOW |   .4228816   .2178255     1.94   0.052    -.0040485    .8498117
       UNEMP |  -.0177077   .5298234    -0.03   0.973    -1.056143    1.020727
      FEMALE |  -.3594406   .2133952    -1.68   0.092    -.7776875    .0588063
         AGE |  -.0990289   .0599753    -1.65   0.099    -.2165784    .0185206
       POLIT |   .1576548   .1634864     0.96   0.335    -.1627727    .4780823
       SOCIO |   .4251983   .2197034     1.94   0.053    -.0054125    .8558091
        POST |    .146854   .0538818     2.73   0.006     .0412477    .2524603
       _cons |  -.4204834   .7236422    -0.58   0.561    -1.838796    .9978291
------------------------------------------------------------------------------
(TRADE==2 is the base outcome)

Simulating main parameters.  Please wait....
% of simulations completed: 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% 55% 60% 65% 70% 75% 80% 85% 90% 95% 100% 

Number of simulations  : 1000
Names of new variables : b1 b2 b3 b4 b5 b6 b7 b8 b9 b10 b11 b12 b13 b14 b15 b16 b17 b18 b19 b20

. setx median

. sum CON_OR, detail

                           CON_OR
-------------------------------------------------------------
      Percentiles      Smallest
 1%           -9             -9
 5%           -7             -9
10%           -5             -9       Obs                 910
25%           -3             -9       Sum of Wgt.         910

50%           -1                      Mean          -1.153846
                        Largest       Std. Dev.      3.335719
75%            0              9
90%            3              9       Variance       11.12702
95%            4              9       Skewness      -.1513762
99%            7              9       Kurtosis       3.516433

. setx CON_OR -3

. simqi,  genpr(pro25 nut25 con25)

Simqi generated the following new variable(s): pro25 nut25 con25

. setx CON_OR 0

. simqi,  genpr(pro75 nut75 con75)

Simqi generated the following new variable(s): pro75 nut75 con75

. 
. gen pro_diff = pro25 - pro75

. gen nut_diff = nut25 - nut75

. gen con_diff = con25 - con75

. 
. centile pro25 nut25 con25 pro75 nut75 con75, centile (50) 

                                                       -- Binom. Interp. --
    Variable |       Obs  Percentile    Centile        [95% Conf. Interval]
-------------+-------------------------------------------------------------
       pro25 |     1,000         50    .1956856        .1934219    .1979577
       nut25 |     1,000         50    .2684631        .2642677    .2714772
       con25 |     1,000         50    .5319623        .5290429      .53512
       pro75 |     1,000         50    .2579036        .2553522    .2602344
       nut75 |     1,000         50    .3001958        .2965365    .3047418
       con75 |     1,000         50    .4378584        .4334049    .4410814

. centile pro_diff nut_diff con_diff, centile (2.5 50 97.5) 

                                                       -- Binom. Interp. --
    Variable |       Obs  Percentile    Centile        [95% Conf. Interval]
-------------+-------------------------------------------------------------
    pro_diff |     1,000        2.5   -.0936751       -.0978414   -.0912764
             |                   50   -.0614645       -.0624869   -.0604714
             |                 97.5   -.0334624       -.0358535   -.0304646
    nut_diff |     1,000        2.5   -.0689708       -.0735749   -.0665127
             |                   50   -.0313066       -.0331284   -.0300683
             |                 97.5      .00459         .001377    .0092577
    con_diff |     1,000        2.5     .052035        .0493649    .0544482
             |                   50    .0934581        .0920332    .0947927
             |                 97.5    .1348031          .13293    .1423144

. 
. 
end of do-file

